Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation

doi:10.1002/aic.16734

This content will become publicly available on July 30, 2020

Title: Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation

Authors:

Wu, Zhe ^[1]; Tran, Anh ^[1]; Rincon, David ^[1];

^[2]

Department of Chemical and Biomolecular EngineeringUniversity of California Los Angeles California
Department of Chemical and Biomolecular EngineeringUniversity of California Los Angeles California, Department of Electrical and Computer EngineeringUniversity of California Los Angeles California

Publication Date:: 2019-07-31

Sponsoring Org.:: USDOE

OSTI Identifier:: 1545905

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: AIChE Journal

Additional Journal Information:: Journal Name: AIChE Journal Journal Volume: 65 Journal Issue: 11; Journal ID: ISSN 0001-1541

Publisher:: Wiley Blackwell (John Wiley & Sons)

Country of Publication:: United States

Language:: English

Citation Formats


                    Wu, Zhe, Tran, Anh, Rincon, David, and Christofides, Panagiotis D. Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation.  United States: N. p., 2019. 
        Web.  doi:10.1002/aic.16734.

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                    Wu, Zhe, Tran, Anh, Rincon, David, & Christofides, Panagiotis D. Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation.  United States.  doi:10.1002/aic.16734.

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                    Wu, Zhe, Tran, Anh, Rincon, David, and Christofides, Panagiotis D. Wed .  
        "Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation".  United States.  doi:10.1002/aic.16734.

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@article{osti_1545905,

  title        = {Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation},

  author       = {Wu, Zhe and Tran, Anh and Rincon, David and Christofides, Panagiotis D.},

  abstractNote = {},

  doi          = {10.1002/aic.16734},

  journal      = {AIChE Journal},

  number       = 11,

  volume       = 65,

  place        = {United States},

  year         = {2019},

  month        = {7}

}

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DOI: 10.1002/aic.16734

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Works referenced in this record:

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Similar Records

This content will become publicly available on July 30, 2020

Title: Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation

Citation Formats

Constrained Nonlinear State Estimation Using Ensemble Kalman Filters journal, March 2010

The Ensemble Kalman Filter: theoretical formulation and practical implementation journal, November 2003

Economic model predictive control of nonlinear process systems using empirical models journal, November 2014

David Draper and E. I. George, and a rejoinder by the authors journal, November 1999

Parallel distributed computing using Python journal, September 2011

The ensemble Kalman filter for combined state and parameter estimation journal, June 2009

A universal formula for stabilization with bounded controls journal, June 1991

Data Driven Economic Model Predictive Control journal, April 2018

Recurrent neural network for solving model predictive control problem in application of four-tank benchmark journal, May 2016

Model Predictive Control of Unknown Nonlinear Dynamical Systems Based on Recurrent Neural Networks journal, August 2012

An Ensemble Kalman Filter for Systems Governed by Differential Algebraic Equations (DAEs) journal, January 2012

On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming journal, April 2005

A framework for parallel and distributed training of neural networks journal, July 2017

Constrained Nonlinear State Estimation Using Ensemble Kalman Filters
journal, March 2010

The Ensemble Kalman Filter: theoretical formulation and practical implementation
journal, November 2003

Economic model predictive control of nonlinear process systems using empirical models
journal, November 2014

David Draper and E. I. George, and a rejoinder by the authors
journal, November 1999

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journal, September 2011

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journal, June 2009

A universal formula for stabilization with bounded controls
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journal, May 2016

Model Predictive Control of Unknown Nonlinear Dynamical Systems Based on Recurrent Neural Networks
journal, August 2012

An Ensemble Kalman Filter for Systems Governed by Differential Algebraic Equations (DAEs)
journal, January 2012

On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
journal, April 2005

A framework for parallel and distributed training of neural networks
journal, July 2017